Assessment of realtime 3D visualization for cardiothoracic diagnostic evaluation and
surgery planning
Bradley M. Hemminger, Phd
Department of Radiology and School of Information and Library Science
University of North Carolina at Chapel Hill
Paul L. Molina, MD
Department of Radiology
University of North Carolina at Chapel Hill
Thomas M. Egan, MD
Department of Surgery
University of North Carolina at Chapel Hill
Frank C. Detterbeck, MD
Department of Surgery
University of North Carolina at Chapel Hill
Keith E. Muller, PhD
Department of Biostatistics
University of North Carolina at Chapel Hill
Christopher S. Coffey, PhD
Department of Biostatistics
University of Alabama
Joseph KT Lee, MD
Department of Radiology
University of North Carolina at Chapel Hill
Supported in part by NIH RO1-CA 44060, NIH PO1-CA 47982, and NIH RO1-CA 60193
Contact Information
Bradley Hemminger
206A Manning Hall, SILS
University of North Carolina
Chapel Hill, NC 27599-3360
(919) 966-2998
(919) 962-8071 (fax)
bmh@ils.unc.edu
Running header: “Hemminger etal, Assessment of realtime 3D visualization”
ABSTRACT
Rationale and Objectives
Three-dimensional (3D) realtime volume rendering has demonstrated improvements in clinical
care for several areas of radiological imaging. We test whether advanced realtime rendering
techniques combined with an effective user interface will allow radiologists and surgeons to
improve their performance for cardiothoracic surgery planning and diagnostic evaluation.
Material and Methods
An interactive combination 3D and 2D visualization system developed at the University of North
Carolina at Chapel Hill was compared against standard tiled 2D slice presentation on a viewbox.
The system was evaluated for 23 complex cardiothoracic CT cases including heart-lung and lung
transplantation, tumor resection, airway stent placement, repair of congential heart defects, aortic
aneurysm repair, and resection of pulmonary arteriovenous malformation. Radiologists and
surgeons recorded their impressions with and without the use of the interactive visualization
system.
Results
The cardiothoracic surgeons reported positive benefits to using the 3D visualizations. The
addition of the 3D visualization changed the surgical plan (65% of cases), increased the surgeon’s
confidence (on average 40% per case), and correlated well with the anatomy found at surgery
(95% of cases). The radiologists reported fewer and less major changes than the surgeons in
their understanding of the case due to the 3D visualization. They found new findings or
additional information about existing findings in 66% of the cases; however, they changed their
radiology report in only 14% of the cases.
Conclusion
With the appropriate choice of 3D real-time volume rendering and a well designed user interface,
both surgeons and radiologists benefit from viewing an interactive 3D visualization in addition to
2D images for surgery planning and diagnostic evaluation of complex cardiothoracic cases. This
study finds that 3D visualization is especially helpful to the surgeon in understanding the case,
and in communicating and planning the surgery. These results suggest that including realtime
3D visualization would be of clinical benefit for complex cardiothoracic CT cases.
Keywords: 3D, volume visualization, surgery planning, diagnostic evaluation, cardiothoracic,
CT, radiology workstation
1
Introduction and Background
Image Display and Analysis
The display of “volume” datasets acquired in CT, MRI and other radiology modalities has
traditionally been done by presenting sets of image “slices” through the volume to the radiologist,
usually in a tiled format. With the advent of faster computers, and advanced computer graphics
algorithms, the possibility of rendering the volume directly as a three dimensional (3D) view of
the body became possible. 3D visualization has already demonstrated benefits in conjunction
with CT including angiography, cerebral aneurysms, dental implants, liver transplants, craniofacial surgery, renal anatomy, radiation treatment planning, temporal bone surgery, virtual
cystoscopy, virtual endoscopy, virtual colonography, virtual bronchoscopy [1-21]. However,
there has been limited work applying realtime 3D volume visualization to CT cardiothoracic
surgery planning and diagnostic evaluation.
A combination of factors has made possible the CT acquisition of high quality volume datasets,
which facilitate 3D volume rendering. The most important change has been the advent of spiral
CT which allows a single scan to quickly cover a sizable 3D volume. Additionally, when
combined with breath-holding by the patient, the 3D stack of slices or volume is well registered
and suitable for high quality 3D visualization techniques [22-24]. Further improvements have
come with multi-slice detectors which support faster scan rates allowing shorter breath holds to
cover more anatomy. Finally, administration of intravenous (IV) contrast material enhances the
contrast difference between vessels of interest and surrounding tissues. Spiral CT studies with
contrast material are standard practice in most departments.
The realtime 3D visualizations evaluated in this study were done using the SeeThru realtime 3D
display application, which was developed in the Radiology department at the University of North
Carolina at Chapel Hill [25-27]. SeeThru runs on an SGI Reality Engine computer graphics
workstation (Silicon Graphics Inc, Mountain View, CA). It was developed to take advantage of
developments in computer graphics hardware architectures and software algorithms to depict 3D
volumes on 2D video monitors at interactive update rates so that the human observer will have a
sense of the 3D scene. The appearance of a 3D scene from a static 2D image can be
accomplished using visual cues such as occlusion, perspective, shading, and stereo (when using
2
stereo display and glasses). Further, if the observer can manipulate the image on the screen,
additional rotational cues are added, as well as strengthening the previous cues [28]. SeeThru
was developed at UNC specifically for radiology and surgery applications [26,27,29]. It supports
the real-time (faster than 20 frames/sec) modification of all viewing parameters (rotation,
zooming, etc.), clipping cutplanes, and classification (ability to select specific tissues types, or
portions of volume). In pilot work leading up to this and other studies [29,30], we found opacity
and gradient based 3D rendering methods to be more effective than previously used visualization
techniques. Surface based renderings [31] have difficulties accurately depicting soft-tissue to
soft-tissue interfaces common in cardiothoracic cases [27]. Simpler, less visually realistic
realtime direct volume rendering techniques like MIP [32,33] can only depict the brightest
contrast location in projections of the volume, limiting their effectiveness to primarily vascular
depictions. Finally, cine loop or still frames of similar high quality direct volume rendered
images are not as effective because they do not provide the crucial kinetic depth effect cues
[25,34]. Further, they do not let the user explore the volume. These tradeoffs were studied by
Ware et al [35], who compared the relative merits of many of the 2D and 3D cues available when
displaying a 3D volume on a computer screen, and found that realtime 3D viewing, combined
with hand control for rotation of the object was the most accurate, as well as one of the fastest
interactions. Additionally, they found cine rotations were slightly worse than user controlled 3D,
while a simple 2D still picture provided the least comprehensible visualization.
Material and Methods
Patients
In our preliminary work we found the 3D visualizations more helpful for complex cardiothoracic
cases. We believe this is due, at least in part, to radiologists’ extensive training which enables
them to recognize and effectively comprehend normal or common anatomy using 2D
visualizations. As a result, we chose to evaluate the 3D visualization only on complex
cardiothoracic cases where we believed it would be most useful clinically. We asked our
cardiothoracic surgeons (THE and FCB) to select cases from the UNC cardiothoracic clinic that
they considered complex. All training and study cases were from these selected complex
cardiothoracic cases. Prior to the study we retrospectively piloted the same acquisition and
visualization methods on 10 patient cases from the cardiothoracic clinic. After finalizing both
scanning and visualization protocols for each of the six case types, we prospectively acquired 23
consecutive adult cases from the cardiothoracic clinic for the study. The cases included six types
2
of cardiothoracic surgical procedures including heart-lung and lung transplantation (n=8), tumor
removal (n=6), airway stent placement (n=6), repair of congential heart defects (n=2), aortic
aneurysm repair (n=1), and resection of pulmonary arteriovenous malformation (n=1). Images
were acquired on a Siemens Somatom Plus spiral CT scanner (Siemens Medical Systems, Iselin,
NJ), using single breath hold spiral acquisitions.
All studies were reviewed and included in the analysis except for those not reviewed by the
surgeons because the transplant recipient died prior to the donors being evaluated. Three cases
had partial data because the transplant recipient patient improved prior to surgery (n=2), or the
planned surgery was ruled out after the review with the 3D visualization (n=1).
CT scanning and reconstruction
The protocol for the cardiothoracic cases was 90 to 120 ml Omnipaque 300 administered via
power injector through the antecubital vein at 2 to 3 ml/sec. Scanning was begun approximately
20 seconds after initiation of contrast administration. All studies are 512X512 pixels in each
individual slice. Zoom factor was set to include the entire chest area, while attempting to
minimize the dead space surrounding the patient imaged. The slice thickness acquisition was one
of 8/8/4, 5/5/3, 3/3/2, where the first number is the rate of CT table feed in mm/sec; the second
number is the acquisition slice thickness in mm; and the third number is the reconstructed
thickness in mm. Scans were generally acquired at the thinnest slice thickness possible that
covered the desired anatomy in a single breath hold of the patient, while not subjecting the patient
to more than the normal radiation dosage. Based on our prior experience and other’s work [23],
we chose to reconstruct spiral datasets in slices spaced approximately half of the acquisition
thickness. The standard Siemens reconstruction algorithms were used to reconstruct the datasets.
For these soft tissue studies the Siemens scanner was set to use the “slim” reconstruction interval
with the “standard” filter.
Visualization Technique
Once the study was acquired on the scanner, it was transmitted electronically over a network
connection to the 3D workstation in one to two minutes. Unlike other methods that require
preprocessing of the data, the SeeThru visualization is available upon receipt of the study by the
3D workstation. This enables the study to be reviewed immediately after the scan is completed,
as well as making possible remote consultation at any computer in the hospital or on the internet.
Patient studies were acquired in 512x512 pixels, of up to 64 slices, creating a 512x512x64
3
volume. These volumes were rendered under the interactive control of the physician on the SGI
workstation. An opacity based direct volume rendering algorithm was used which did not
incorporate gradient information and had a fixed light source. All aspects of the visualization
including rotation, zoom, transparency, and classification were under interactive control via the
mouse and user interface, with the screen updated at approximately 10 frames per second. A
complete description of our hardware and software methods is described in Hemminger [27].
Figure 1 shows the Seethru interface with a living lung donor case with the airways classification
preset setting. Note that Seethru shows both 3D and 2D presentations, with the 2D slices
presented via three multiplanar reformatted windows, which can scroll through the study from
three orthogonal axes. In this paper we will refer to the Seethru visualization as the 3D
visualization because the primary usage of Seethru in the study by the surgeons and radiologists
was of the 3D volume rendered window. Protocols were developed for each type of study for
which 3D visualizations were utilized. This enabled each study to automatically come up with
preset visualization settings appropriate for the individual case type. The clinician would then
adjust the viewing parameters to optimize the visualization for the anatomy of the specific case.
This usually took 10-20 seconds. Then the clinician would spend time rotating, zooming and
cutting away the anatomy, as well as utilizing different classifications (for instance airway and
vascular classifications on living lung donor transplant cases) as part of the treatment planning.
Figure 1
The realtime 3D visualizations and interactive 2D MPRs that were utilized by Seethru in this
study, are now available on most major commercial systems supporting opacity based 3D
realtime or near realtime volume rendering, including such products as GE Advantage Windows,
Siemens Virtuoso, Vital Images Vitrea, and Voxar PlugNView 3D.
Study Design
Because of patient care considerations, the study design was for the computer based 3D volume
rendered plus 2D stack visualization (Seethru), to be evaluated as an adjunct to the existing
standard of care, the tiled 2D film presentation. Thus, both the radiologist and the surgeons
would see cases as they normally would, and then in addition they would review the case with the
4
Seethru visualization tool available. The overall hypothesis was that the addition of the 3D
visualization would change (improve) clinical care. Separate questionnaires were designed for
the radiologist and surgeon to elucidate how the addition of the 3D visualization caused
quantitative changes (changes to the surgery plan, changes to the radiologist report, changes in
radiologist reported findings) as well as qualitative changes (radiologist’s understanding of the
case, radiologist communication of the case to the surgeon, correlation of anatomy seen in 3D
with patient at surgery, surgeon’s understanding of the case, surgeon’s confidence in their
surgical plan before and after using the 3D). The complete questionnaires are available on the
web [36,37]. We chose not to try to analyze the length of viewing times because they are highly
variable due to the consultation type setting, the surgeon’s use of the 3D visualization for
treatment planning while viewing the study, interruptions, etc.
Protocol
First, the radiologist would review the films on the body CT alternator viewbox as part of their
standard clinical routine. When the surgeon was available, he would contact the radiologist and
they would review the films on a viewbox in their standard manner. This was the standard
clinical procedure. Then, after they had decided their initial opinions of the case, they would both
review the 3D visualization of the case together on the workstation. The films remained available
on an adjacent viewbox for comparison. After this review the radiologist and surgeon filled out
their questionnaires. Lastly, after the surgery was performed, the surgeon completed the
remaining questions on the surgery form that correlated surgical findings with what they saw
earlier on the 3D visualization. No time limits were placed on any of the viewings or analyses.
Responses from the forms were coded by the experimenters into computer data files, which were
then analyzed using SAS (Cary NC).
Observers
There were three surgeons who participated in the study. One surgeon participated in only one
case, with the rest divided fairly evenly between the other two surgeons. All three surgeons were
senior faculty, with extensive experience. There was a single radiologist participant, who was the
senior thoracic radiologist and the primary reader for cardiothoracic cases in the department.
Results:
5
The results from the radiologist’s and surgeon’s study questionnaires are presented first in tabular
form (tables 1-8). Additionally, an analysis was performed to attempt to put a greater statistical
level of significance on these results. For the dichotomous variables (change of radiologist
report, change of surgical plan) 95% confidence limits were placed on the proportion of positive
responses following the method described in Johnson and Kotz [38] (tables 1 and 2). Table 3
summarizes across all 23 cases the changes in radiologist’s individual findings, grouped by
significance of finding. The remaining categorical variables are displayed as histogram tables
counted across the twenty-three cases in the study. The 95% confidence intervals about the
median are given as well. Because the data for these variables is discrete and the sample size for
this study is small, the median is the best measure of central tendency. The confidence intervals
were derived using a method based on randomization [39]. These results for the categorical
variables are shown in tables 4-8.
Dichotomous Variables
The results reveal that 13.0% of the radiologists’ reports were changed due to the 3D
visualization (the 95% confidence interval is [2.8,28.0]). These results are shown in Table 1. An
example of a changed radiologist report was a case where the 3D visualization demonstrated
“narrowing of the origin of the left carotid artery [which was] not appreciated on 2D images”.
Table 1.
In comparison, surgeons changed their surgical plan in 65.2% of the cases as a result of the 3D
visualization (the 95% confidence interval is [42.7,80.3]). These results are shown in Table 2. If
the surgery plan was changed, the surgeons were required to list the changes to their surgical
plan. For example for a severe bronchial stenosis case the surgeon indicated four changes
occurred because of seeing the 3D visualization: “1) was more confident the patient would
survive operative intervention; 2) made an approach to the right bronchus seem feasible; 3)
planned to use the silastic rather than wire stent; 3) realized distal airways were patent.”
Table 2.
The radiologists were asked to list the individual findings in each case and rate how well they
were seen with the 3D visualization compared to without the 3D visualization. Their ratings for
all the findings are summarized across the cases and shown in table 3. In a heart lung transplant
6
for a congential heart defect case the radiologist listed two findings: the “complex cardiovascular
anatomy relationship” was seen better on 3D, and the “narrowed origin of a great vessel off the
interrupted aortic arch” was seen exclusively on the 3D visualization. In a living lung donor case,
the radiologist reported that the “precise identification and delineation of bilateral middle and
lower lobe pulmonary vasculature and its relationship to corresponding airway anatomy” was
seen exclusively on the 3D visualization, and that “small pulmonary vessels” were seen better in
the 3D visualization.
Table 3.
Categorical Variables
The 95% confidence interval for the radiologist’s question “Was 3D visualization helpful in your
understanding the case?” was [‘no difference’, ‘slightly better’], with the results shown in Table
4.
Table 4.
For the radiologist’s question of “Was the 3D visualization clinically helpful to the radiologist
when conveying your understanding of a case to the surgeon?” the 95% CI was [‘slightly better’,
‘moderately better’]. These results are seen in table 5.
Table 5.
For the surgeon, the 95% CI for how well the 3D visualization corresponded with what the
surgeon saw at surgery was [‘moderately good match’, ‘very good match’]. These results are
tabulated in table 6. There was only one case in which a good match was not reported between
the 3D visualization and what was seen at surgery. In that case, the surgeon reported “we missed
a large posterior ascending branch [connecting] the right upper lobe coming off the superior
segment artery to the lower lobe”. This is a drawback of the 2D display. While allowing the
observer to rotate the anatomy on the 2D display screen gives a good sense of the 3D anatomy, it
is not as conveniently appreciated as on a true 3D display. In the future, a true 3D visualization
display technology might help reduce this type of error.
7
Table 6.
For the surgeon’s question “Was the 3D visualization clinically helpful based on what was seen at
surgery” the 95% CI is [‘slightly better’, ‘significantly better’], and summarized in table 7. In
every case the surgeons indicated the 3D visualization helped clinically. An example of this from
comments written on the form by the surgeon in a heart-lung transplant case: “It [the 3D
visualization] was essential. I doubt we would have been able to perform the procedure
successfully without it—and this information could not be gotten from the 2D CT. An angiogram
might have provided some of the information”.
Table 7.
The last question analyzed on the surgical report asked what the surgeon’s confidence in their
surgical plan was before and after using the 3D visualization. The addition of the 3D
visualization always resulted in increased confidence in the surgical plan. The mean confidence
prior to the addition of the 3D visualization was 45% (out of 100%); after the addition of 3D is
was 86%. The 95% CI for the difference in surgeon’s confidence was [30%, 40%], indicating a
substantial improvement in the surgeon’s confidence in his surgical plan after viewing the 3D
visualization.
In all areas investigated, the cardiothoracic surgeons reported positive benefits to using the 3D
visualizations. In all but one case, the surgeons indicated the 3D visualization was clinically
helpful. In over two thirds of the cases the surgical plan was altered due to the 3D visualization,
and in all the remaining cases the surgeons still listed items that they understood or appreciated
better because of the 3D visualization. When the surgical plan did change, it was often a very
significant change. For instance, a heart lung transplant case with aortic arch reconstruction
would not have occurred without the 3D visualization. The surgeon’s case comment was that the
“3D visualization was essential. [they] doubt they would have been able to perform the
procedure successfully without it—and this information could not have been gotten from the 2D
CT. An angiogram might have provided some of the same information.” Similarly, a planned
sleeve lobectomy surgery was canceled after the 3D visualization because the surgeons “decided
that sleeve lobectomy was not possible because of extensive pulmonary artery involvement by
tumor below the upper lobe takeoff.”
8
While the radiologists reported fewer and less major changes than the surgeons in their
understanding of the case due to the 3D visualization, the changes were still statistically
significant. In over half the cases the radiologists reported improvements in their confidence and
understanding. The best measure of how significant the 3D visualization was to the radiologist is
whether it affected their radiological report. In over 20% of the cases it changed their report.
Figure 2 shows an example still image from a lung mass resection case, and Figure 3 shows a
lung donor airway evaluation case with the preset classification setting emphasizing the main
vascular vessels.
While viewing times were not recorded, in most cases the first 30 seconds were spent bringing up
the study, verifying the correct patient, and checking the initial preset visualizations and fine
tuning them as needed. Then the radiologists and surgeon reviewed the case, primarily in the 3D
window, but also comparing with the 2D MPRs. The bulk of the time was spent at the end of the
session by the surgeon, using the 3D visualization as a model to plan the surgery, and to facilitate
communication among the surgery and radiology staff present.
Figure 2.
Figure 3.
During the study comments were recorded by the experimenter (BMH) and at the end of the study
the physicians were interviewed regarding the use of the 3D visualization as part of their clinical
routine. Interviewees included other surgical staff participating in the surgery planning and
operation, as well as the lead surgeons and the radiologist. These interviews and analysis of the
questionnaires provided several highlights:

Non-radiologist medical staff seemed to benefit significantly from the 3D presentation.
Surgeons, in particular, seemed comfortable using the 3D visualization, perhaps because
9
it is closely tied to their clinical experience in seeing and manipulating parts of the body
in 3D.

Experienced radiologists did not benefit as much as the surgeons except for complex or
uncommon anatomy. This may be because their extensive training in viewing the 2D
films allows them to answer the questions they are expected to address (e.g. is the PA
involved or not?), while the surgeon has to make detailed technical decisions that benefit
from the 3D visualization (e.g., is the involvement of the PA distal enough to permit a
resection?).

In addition to using the 3D visualization for understanding anatomy, the surgeons made
extensive use of the 3D representation to communicate with other surgeons and
radiologists when trying to understand the anatomy and plan the surgical procedure.

The typical scenario for using SeeThru to assist in cardiothoracic surgical planning was
to classify the volume to the appropriate areas of interest; then cut away and rotate the
volume to get the best view; then cut back and forth through the 3D volume to see
interior objects better, especially cutting in and out from an angle similar to the planned
surgical entry.

The ability to conveniently bring up the study immediately after the scan, and see and
interact with the study in 3D to visualize information not seen on the 2D slices, were
reasons the cardiothoracic surgeons gave for using SeeThru.
Discussion
There can be significant variations in the clinical usefulness of 3D visualization depending on the
choice of clinical acquisition parameters, the rendering algorithms, and the computer human
interaction methods. With some experience, reasonable choices for these variables can be made.
We have found that using real-time volume rendered 3D visualization as implemented in Seethru,
under the interactive control of the clinician is clinically useful for cardiothoracic surgery
planning and diagnostic evaluation in complex cases. The use of such a 3D visualization tool was
of clear benefit to the surgeons, who indicated they would use it clinically. In the study, the
addition of the 3D visualization was almost always reported to be clinically helpful, and in two
thirds of the cases caused the surgeon to change their surgical plan. Further, the surgeon’s
confidence in their surgical plan improved significantly in almost every case. The radiologist
benefited, but to a lesser extent. The radiologist’s findings were better comprehended, or
10
sometimes exclusively found using the 3D visualization. Their radiology reports changed 20%
of the time due to the 3D visualization.
An important contribution of the 3D visualization that we did not anticipate was its use in
supporting the discussion of the diagnosis and planned surgical procedures, allowing a group
consensus to be formed while viewing and manipulating the model of the anatomy. These results
mesh well with other findings showing realtime 3D visualization to be helpful for surgery
planning and diagnostic evaluation. This study demonstrates the positive effect of 3D
visualization in cardiothoracic surgery planning, which is a difficult visualization problem
because of the small contrast differences in the soft tissue to soft tissue boundaries. These results
suggest that using 3D visualization for complex cardiothoracic surgical planning is of significant
benefit, especially to the surgeons, and should be used routinely. From a practical standpoint, it
suggests that surgeons should have 3D visualization capabilities on their primary workstation,
and that radiologists should have convenient access on their standard PACS or a nearby
workstation.
Future Work
Seethru utilizes both the interactive 3D visualization and the interactive 2D MPR views. Each of
these can contribute to the 3D understanding of the anatomy. Since most all of today’s PACS
workstations support interactive 2D slices through the scanned volume, but not necessarily
interactive 3D visualization, it would be interesting to study how much of the improvement in a
combined 3D+2D interactive visualization could be achieved with only the less expensive, more
commonly available 2D only PACS workstation.
Acknowledgements
This work was supported in part by NIH R01 CA 44060, NIH P01 47982, and NIH R01CA60193.
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